{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 案例：信用卡交易欺诈数据预测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1 背景\n",
    "#### 有一批交易数据，数据总量达到28万多条。其中，正常交易数据量占比99.83%，欺诈交易数据量仅占比0.17%，此案例属于一个典型的不平衡数据案例。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 目标\n",
    "#### 训练出一个模型，能够判断出欺诈交易数据。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3 案例流程\n",
    "\n",
    "### 方案一：\n",
    "#### ① 针对不平衡数据案例，这里采取的是under_sample（下采样）方式，使得正负样例的数据均衡；\n",
    "#### ② 由于欺诈的交易数据有：492条，因此，我们将从正常的交易数据中随机选择492条。这样新的数据集将有984条数据；\n",
    "#### ③ 基于新的数据集，我们分成：train, test 进行训练、预测。最终，利用集成学习技术来进行预测；\n",
    "#### ④ 采用交叉验证的技术，进行训练、预测；\n",
    "#### ⑤ 绘制学习曲线；\n",
    "#### ⑥ 绘制ROC曲线；"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载数据文件\n",
    "df = pd.read_csv(\"creditcard.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看默认的前5行数据\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   Time    284807 non-null  float64\n",
      " 1   V1      284807 non-null  float64\n",
      " 2   V2      284807 non-null  float64\n",
      " 3   V3      284807 non-null  float64\n",
      " 4   V4      284807 non-null  float64\n",
      " 5   V5      284807 non-null  float64\n",
      " 6   V6      284807 non-null  float64\n",
      " 7   V7      284807 non-null  float64\n",
      " 8   V8      284807 non-null  float64\n",
      " 9   V9      284807 non-null  float64\n",
      " 10  V10     284807 non-null  float64\n",
      " 11  V11     284807 non-null  float64\n",
      " 12  V12     284807 non-null  float64\n",
      " 13  V13     284807 non-null  float64\n",
      " 14  V14     284807 non-null  float64\n",
      " 15  V15     284807 non-null  float64\n",
      " 16  V16     284807 non-null  float64\n",
      " 17  V17     284807 non-null  float64\n",
      " 18  V18     284807 non-null  float64\n",
      " 19  V19     284807 non-null  float64\n",
      " 20  V20     284807 non-null  float64\n",
      " 21  V21     284807 non-null  float64\n",
      " 22  V22     284807 non-null  float64\n",
      " 23  V23     284807 non-null  float64\n",
      " 24  V24     284807 non-null  float64\n",
      " 25  V25     284807 non-null  float64\n",
      " 26  V26     284807 non-null  float64\n",
      " 27  V27     284807 non-null  float64\n",
      " 28  V28     284807 non-null  float64\n",
      " 29  Amount  284807 non-null  float64\n",
      " 30  Class   284807 non-null  int64  \n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "# 查看数据的信息\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
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       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>284807.000000</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>2.848070e+05</td>\n",
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       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>1.165980e-15</td>\n",
       "      <td>3.416908e-16</td>\n",
       "      <td>-1.373150e-15</td>\n",
       "      <td>2.086869e-15</td>\n",
       "      <td>9.604066e-16</td>\n",
       "      <td>1.490107e-15</td>\n",
       "      <td>-5.556467e-16</td>\n",
       "      <td>1.177556e-16</td>\n",
       "      <td>-2.406455e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.656562e-16</td>\n",
       "      <td>-3.444850e-16</td>\n",
       "      <td>2.578648e-16</td>\n",
       "      <td>4.471968e-15</td>\n",
       "      <td>5.340915e-16</td>\n",
       "      <td>1.687098e-15</td>\n",
       "      <td>-3.666453e-16</td>\n",
       "      <td>-1.220404e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.194353e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.345240e-01</td>\n",
       "      <td>7.257016e-01</td>\n",
       "      <td>6.244603e-01</td>\n",
       "      <td>6.056471e-01</td>\n",
       "      <td>5.212781e-01</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.616051e+01</td>\n",
       "      <td>-4.355724e+01</td>\n",
       "      <td>-7.321672e+01</td>\n",
       "      <td>-1.343407e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.483038e+01</td>\n",
       "      <td>-1.093314e+01</td>\n",
       "      <td>-4.480774e+01</td>\n",
       "      <td>-2.836627e+00</td>\n",
       "      <td>-1.029540e+01</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-7.682956e-01</td>\n",
       "      <td>-5.540759e-01</td>\n",
       "      <td>-2.086297e-01</td>\n",
       "      <td>-6.430976e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.283949e-01</td>\n",
       "      <td>-5.423504e-01</td>\n",
       "      <td>-1.618463e-01</td>\n",
       "      <td>-3.545861e-01</td>\n",
       "      <td>-3.171451e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-2.741871e-01</td>\n",
       "      <td>4.010308e-02</td>\n",
       "      <td>2.235804e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.945017e-02</td>\n",
       "      <td>6.781943e-03</td>\n",
       "      <td>-1.119293e-02</td>\n",
       "      <td>4.097606e-02</td>\n",
       "      <td>1.659350e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>3.985649e-01</td>\n",
       "      <td>5.704361e-01</td>\n",
       "      <td>3.273459e-01</td>\n",
       "      <td>5.971390e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.863772e-01</td>\n",
       "      <td>5.285536e-01</td>\n",
       "      <td>1.476421e-01</td>\n",
       "      <td>4.395266e-01</td>\n",
       "      <td>3.507156e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>7.330163e+01</td>\n",
       "      <td>1.205895e+02</td>\n",
       "      <td>2.000721e+01</td>\n",
       "      <td>1.559499e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.720284e+01</td>\n",
       "      <td>1.050309e+01</td>\n",
       "      <td>2.252841e+01</td>\n",
       "      <td>4.584549e+00</td>\n",
       "      <td>7.519589e+00</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  1.165980e-15  3.416908e-16 -1.373150e-15  2.086869e-15   \n",
       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
       "\n",
       "                 V5            V6            V7            V8            V9  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   9.604066e-16  1.490107e-15 -5.556467e-16  1.177556e-16 -2.406455e-15   \n",
       "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
       "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
       "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
       "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
       "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
       "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
       "\n",
       "       ...           V21           V22           V23           V24  \\\n",
       "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   ...  1.656562e-16 -3.444850e-16  2.578648e-16  4.471968e-15   \n",
       "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
       "\n",
       "                V25           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean   5.340915e-16  1.687098e-15 -3.666453e-16 -1.220404e-16      88.349619   \n",
       "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数据的描述\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      0\n",
       "V1        0\n",
       "V2        0\n",
       "V3        0\n",
       "V4        0\n",
       "V5        0\n",
       "V6        0\n",
       "V7        0\n",
       "V8        0\n",
       "V9        0\n",
       "V10       0\n",
       "V11       0\n",
       "V12       0\n",
       "V13       0\n",
       "V14       0\n",
       "V15       0\n",
       "V16       0\n",
       "V17       0\n",
       "V18       0\n",
       "V19       0\n",
       "V20       0\n",
       "V21       0\n",
       "V22       0\n",
       "V23       0\n",
       "V24       0\n",
       "V25       0\n",
       "V26       0\n",
       "V27       0\n",
       "V28       0\n",
       "Amount    0\n",
       "Class     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查是否有空置\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      float64\n",
       "V1        float64\n",
       "V2        float64\n",
       "V3        float64\n",
       "V4        float64\n",
       "V5        float64\n",
       "V6        float64\n",
       "V7        float64\n",
       "V8        float64\n",
       "V9        float64\n",
       "V10       float64\n",
       "V11       float64\n",
       "V12       float64\n",
       "V13       float64\n",
       "V14       float64\n",
       "V15       float64\n",
       "V16       float64\n",
       "V17       float64\n",
       "V18       float64\n",
       "V19       float64\n",
       "V20       float64\n",
       "V21       float64\n",
       "V22       float64\n",
       "V23       float64\n",
       "V24       float64\n",
       "V25       float64\n",
       "V26       float64\n",
       "V27       float64\n",
       "V28       float64\n",
       "Amount    float64\n",
       "Class       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数据类型\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看Class分布\n",
    "labels = ['Not Fraud', 'Fraud'] # 标签\n",
    "size = df['Class'].value_counts() # 统计class的类别数量\n",
    "colors = ['lightgreen', 'orange'] # 颜色\n",
    "explode = [0, 0.1] # 饼图突出\n",
    "plt.figure(figsize=(9,9)) # 画布大小\n",
    "plt.pie(size, colors=colors, explode=explode, labels=labels, shadow=True, autopct='%.2f%%') # 饼图参数设置\n",
    "plt.axis('off') # 关闭坐标轴\n",
    "plt.title(\"Data Distribution\") # 标题\n",
    "plt.legend() # 显示标签\n",
    "plt.show() # 显示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 显示交易时间、交易金额的分布\n",
    "fig, ax = plt.subplots(1, 2, figsize=(18, 4)) # 画布大小\n",
    "\n",
    "amout_val = df['Amount'].values # 交易金额的数值\n",
    "time_val = df['Time'].values # 交易时间的数值\n",
    "\n",
    "sns.distplot(amout_val, ax=ax[0], color='r') # 参数设置\n",
    "ax[0].set_title('amount distribution', fontsize=14) # 标题\n",
    "ax[0].set_xlim(min(amout_val), max(amout_val)) # 横轴设置\n",
    "\n",
    "sns.distplot(time_val, ax=ax[1], color='b') # 参数设置\n",
    "ax[1].set_title('time distribution', fontsize=14) # 标题\n",
    "ax[1].set_xlim(min(time_val), max(time_val)) # 横轴设置\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据表中的大多数列的数据已经归一化，接下来对Amount进行归一化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "df['scaled_amount'] = StandardScaler().fit_transform(df['Amount'].values.reshape(-1,1)) # 金额归一化\n",
    "df['scaled_time'] = StandardScaler().fit_transform(df['Time'].values.reshape(-1,1)) # 时间归一化\n",
    "\n",
    "df.drop(['Amount', 'Time'], axis=1, inplace=True) # 删除原始的数据列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>...</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Class</th>\n",
       "      <th>scaled_amount</th>\n",
       "      <th>scaled_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>0.090794</td>\n",
       "      <td>...</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>0</td>\n",
       "      <td>0.244964</td>\n",
       "      <td>-1.996583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>-0.166974</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.342475</td>\n",
       "      <td>-1.996583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>0.207643</td>\n",
       "      <td>...</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>0</td>\n",
       "      <td>1.160686</td>\n",
       "      <td>-1.996562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>-0.054952</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>0</td>\n",
       "      <td>0.140534</td>\n",
       "      <td>-1.996562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>0.753074</td>\n",
       "      <td>...</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.073403</td>\n",
       "      <td>-1.996541</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9       V10  ...       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  0.090794  ...  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425 -0.166974  ... -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  0.207643  ...  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024 -0.054952  ...  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  0.753074  ...  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Class  scaled_amount  scaled_time  \n",
       "0 -0.189115  0.133558 -0.021053      0       0.244964    -1.996583  \n",
       "1  0.125895 -0.008983  0.014724      0      -0.342475    -1.996583  \n",
       "2 -0.139097 -0.055353 -0.059752      0       1.160686    -1.996562  \n",
       "3 -0.221929  0.062723  0.061458      0       0.140534    -1.996562  \n",
       "4  0.502292  0.219422  0.215153      0      -0.073403    -1.996541  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() # 默认显示前5行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# under_sampling 方法\n",
    "\n",
    "normal_data_indices = df[df['Class']==0].index # 正常交易数据的索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "284315"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 正常交易数据的数量\n",
    "len(normal_data_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "492"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fraud_data_number = len(df[df['Class']==1]) # 欺诈交易数据的数量\n",
    "\n",
    "fraud_data_number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "fraud__data_indices = df[df['Class']==1].index # 欺诈交易数据的索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从正常交易数据中随机选择492个，与欺诈交易数据组成一个新的数据集，使得两者的数量均衡\n",
    "\n",
    "random_normal_data_indices = np.random.choice(normal_data_indices, fraud_data_number, replace=False) # replace=False,表示不放回随机抽样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "492"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(random_normal_data_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 合并 随机抽取的正常交易数据的索引 和 欺诈交易数据的索引\n",
    "under_sample_indices = np.concatenate([fraud__data_indices, random_normal_data_indices])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# under sample dataset 下采样的数据集\n",
    "under_sample_dataset = df.iloc[under_sample_indices, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据集 和 标签集\n",
    "X_under_sample = under_sample_dataset.drop('Class', axis=1) # 数据集\n",
    "y_under_sample = under_sample_dataset['Class'] # 标签集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(984, 30)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_under_sample.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(984,)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_under_sample.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "欺诈交易的占比： 50.0 %\n"
     ]
    }
   ],
   "source": [
    "print(\"欺诈交易的占比：\", under_sample_dataset[under_sample_dataset['Class']==1].shape[0] / under_sample_dataset.shape[0] * 100,\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "正常交易的占比： 50.0 %\n"
     ]
    }
   ],
   "source": [
    "print(\"正常交易的占比：\", under_sample_dataset[under_sample_dataset['Class']==0].shape[0] / under_sample_dataset.shape[0] * 100,\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看新的数据集的分布\n",
    "sns.countplot('Class', data=under_sample_dataset)\n",
    "plt.title(\"Class\", fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 原始的整个数据集分割 : train, test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据集分割：训练集和测试集\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = df.drop('Class', axis=1) # 数据集\n",
    "y = df['Class'] # 标签\n",
    "\n",
    "#kFold = StratifiedKFold(n_splits=5, random_state=666, shuffle=True) # 5折交叉验证\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=666)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据集\n",
    "X_train = X_train.values\n",
    "X_test = X_test.values\n",
    "# 标签\n",
    "y_train = y_train.values\n",
    "y_test = y_test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(227845, 30)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(56962, 30)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 新的数据集分割：train, test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_new_train, X_new_test, y_new_train, y_new_test = train_test_split(X_under_sample, y_under_sample, test_size=0.2, random_state=666)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(787, 30)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(197, 30)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 关系矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(2, 1, figsize=(24,20)) # 画布大小\n",
    "\n",
    "# 整个DataFrame\n",
    "corr = df.corr() # 相关系数\n",
    "sns.heatmap(corr,cmap='coolwarm_r', annot_kws={'size':20},ax=ax1)\n",
    "ax1.set_title(\"imbalanced metrix\", fontsize=14)\n",
    "\n",
    "# 新的DataFrame\n",
    "df_corr = under_sample_dataset.corr() \n",
    "sns.heatmap(df_corr, cmap='coolwarm_r',annot_kws={'size':20}, ax=ax2)\n",
    "ax2.set_title(\"balanced matrix\", fontsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 简单分类器实现\n",
    "from sklearn.linear_model import LogisticRegression # 逻辑回归\n",
    "from sklearn.linear_model import SGDClassifier      # 随机梯度\n",
    "from sklearn.neighbors import KNeighborsClassifier  # K近邻\n",
    "from sklearn.svm import SVC                         # 支撑向量机\n",
    "from sklearn.tree import DecisionTreeClassifier     # 决策树\n",
    "from sklearn.ensemble import RandomForestClassifier # 随机森林\n",
    "from sklearn.model_selection import cross_val_score # 交叉验证计算accuracy\n",
    "from sklearn.model_selection import GridSearchCV    # 网格搜索，获取最优参数\n",
    "from sklearn.model_selection import StratifiedKFold # 交叉验证\n",
    "from collections import Counter\n",
    "# 评估指标\n",
    "from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, roc_auc_score, accuracy_score, classification_report\n",
    "\n",
    "from sklearn.ensemble import BaggingClassifier, GradientBoostingClassifier # 集成学习\n",
    "from xgboost import XGBClassifier                   # 极限梯度提升树\n",
    "\n",
    "classifiers = {\n",
    "    'LogisticRegression':LogisticRegression(), # 逻辑回归\n",
    "    \"SVC\":SVC(),                               # 支撑向量机\n",
    "    \"KNN\":KNeighborsClassifier(),              # K近邻\n",
    "    'DT':DecisionTreeClassifier(),             # 决策树\n",
    "    'RFC':RandomForestClassifier(),            # 随机森林\n",
    "    'Bagging':BaggingClassifier(),             # 集成学习bagging\n",
    "    'SGD':SGDClassifier(),                     # 随机梯度\n",
    "    'GBC':GradientBoostingClassifier(),        # 集成学习Gradient\n",
    "    'xgb':XGBClassifier()                      # 极限梯度提升树\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy_score(X_train, y_train):\n",
    "    for key, classifier in classifiers.items(): # 遍历每一个分类器，分别训练、计算得分\n",
    "        classifier.fit(X_train, y_train)\n",
    "        training_score = cross_val_score(classifier, X_train, y_train, cv=5) # 5折交叉验证\n",
    "        print(\"Classifier Name : \", classifier.__class__.__name__,\"  Training Score ：\", round(training_score.mean(), 2)*100,'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1 最简单的交叉验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier Name :  LogisticRegression   Training Score ： 100.0 %\n",
      "Classifier Name :  SVC   Training Score ： 100.0 %\n",
      "Classifier Name :  KNeighborsClassifier   Training Score ： 100.0 %\n",
      "Classifier Name :  DecisionTreeClassifier   Training Score ： 100.0 %\n",
      "Classifier Name :  RandomForestClassifier   Training Score ： 100.0 %\n",
      "Classifier Name :  BaggingClassifier   Training Score ： 100.0 %\n",
      "Classifier Name :  SGDClassifier   Training Score ： 100.0 %\n",
      "Classifier Name :  GradientBoostingClassifier   Training Score ： 100.0 %\n",
      "Classifier Name :  XGBClassifier   Training Score ： 100.0 %\n"
     ]
    }
   ],
   "source": [
    "# 1.1 使用原始数据集【注意：不可取】\n",
    "accuracy_score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier Name :  LogisticRegression   Training Score ： 95.0 %\n",
      "Classifier Name :  SVC   Training Score ： 94.0 %\n",
      "Classifier Name :  KNeighborsClassifier   Training Score ： 93.0 %\n",
      "Classifier Name :  DecisionTreeClassifier   Training Score ： 91.0 %\n",
      "Classifier Name :  RandomForestClassifier   Training Score ： 94.0 %\n",
      "Classifier Name :  BaggingClassifier   Training Score ： 94.0 %\n",
      "Classifier Name :  SGDClassifier   Training Score ： 93.0 %\n",
      "Classifier Name :  GradientBoostingClassifier   Training Score ： 93.0 %\n",
      "Classifier Name :  XGBClassifier   Training Score ： 94.0 %\n"
     ]
    }
   ],
   "source": [
    "# 1.2 使用新的数据集\n",
    "accuracy_score(X_new_train, y_new_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 网格搜索：获取最优超参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 网格搜索：获取最优超参数\n",
    "# 1 LR\n",
    "def LR_gs(X_train, y_train):\n",
    "    # LR\n",
    "    LR_param = {\n",
    "        'penalty':['l1', 'l2'],\n",
    "        'C':[0.001, 0.01, 0.1, 1, 10]\n",
    "    }\n",
    "\n",
    "    LR_gs = GridSearchCV(LogisticRegression(),param_grid=LR_param, n_jobs=-1, scoring='accuracy')\n",
    "    LR_gs.fit(X_train, y_train)\n",
    "\n",
    "    LR_estimators = LR_gs.best_estimator_ # 最优参数\n",
    "    \n",
    "    return LR_estimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2 KNN\n",
    "def KNN_gs(X_train, y_train):\n",
    "    KNN_param = {\n",
    "        'n_neighbors':list(range(2, 5, 1)),\n",
    "        'algorithm':['auto','ball_tree','kd_tree','brute']\n",
    "    }\n",
    "\n",
    "    KNN_gs = GridSearchCV(KNeighborsClassifier(), param_grid=KNN_param, n_jobs=-1, scoring='accuracy')\n",
    "    KNN_gs.fit(X_train, y_train)\n",
    "\n",
    "    KNN_estimators = KNN_gs.best_estimator_ # 最优参数\n",
    "    \n",
    "    return KNN_estimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3 SVC\n",
    "def SVC_gs(X_train, y_train):\n",
    "    SVC_param = {\n",
    "        'C':[0.5, 0.7, 0.9, 1],\n",
    "        'kernel':['rfb', 'poly', 'sigmod', 'linear']\n",
    "    }\n",
    "\n",
    "    SVC_gs = GridSearchCV(SVC(), param_grid=SVC_param, n_jobs=-1, scoring='accuracy')\n",
    "    SVC_gs.fit(X_train, y_train)\n",
    "\n",
    "    SVC_estimators = SVC_gs.best_estimator_ # 最优参数\n",
    "    \n",
    "    return SVC_estimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4 DT\n",
    "def DT_gs(X_train, y_train):\n",
    "    DT_param = {\n",
    "        'criterion':['gini', 'entropy'], # 衡量标准\n",
    "        'max_depth':list(range(2, 5, 1)), # 树的深度\n",
    "        'min_samples_leaf':list(range(3, 7, 1)) # 最小叶子节点数\n",
    "    }\n",
    "\n",
    "    DT_gs = GridSearchCV(DecisionTreeClassifier(), param_grid=DT_param, n_jobs=-1, scoring='accuracy')\n",
    "    DT_gs.fit(X_train, y_train)\n",
    "\n",
    "    DT_estimators = DT_gs.best_estimator_ # 最优参数\n",
    "    \n",
    "    return DT_estimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5 RFC\n",
    "def RFC_gs(X_train, y_train):\n",
    "    RFC_param = {\n",
    "        'n_estimators':[100, 150, 200], # 多少棵树\n",
    "        'criterion':['gini', 'entropy'], # 衡量标准\n",
    "        'max_depth':list(range(2,5,1)), # 树的深度\n",
    "    }\n",
    "    \n",
    "    RFC_gs = GridSearchCV(RandomForestClassifier(), param_grid=RFC_param, n_jobs=-1, scoring='accuracy')\n",
    "    RFC_gs.fit(X_train, y_train)\n",
    "    \n",
    "    RFC_estimators = RFC_gs.best_estimator_\n",
    "    \n",
    "    return RFC_estimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 6 Bag\n",
    "def BAG_gs(X_train, y_train):\n",
    "    BAG_param = {\n",
    "        'n_estimators':[10, 15, 20]\n",
    "    }\n",
    "    \n",
    "    BAG_gs = GridSearchCV(BaggingClassifier(), param_grid=BAG_param, n_jobs=-1, scoring='accuracy')\n",
    "    BAG_gs.fit(X_train, y_train)\n",
    "    \n",
    "    BAG_estimators = BAG_gs.best_estimator_\n",
    "    \n",
    "    return BAG_estimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 7 SGD\n",
    "def SGD_gs(X_train, y_train):\n",
    "    SGD_param = {\n",
    "        'penalty':['l2','l1'],\n",
    "        'max_iter':[1000, 1500, 2000]\n",
    "    }\n",
    "    \n",
    "    SGD_gs = GridSearchCV(SGDClassifier(), param_grid=SGD_param, n_jobs=-1, scoring='accuracy')\n",
    "    SGD_gs.fit(X_train, y_train)\n",
    "    \n",
    "    SGD_estimators = SGD_gs.best_estimator_\n",
    "    \n",
    "    return SGD_estimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 8 xgb\n",
    "def XGB_gs(X_train, y_train):\n",
    "    XGB_param = {\n",
    "        'max_depth':[3,4,5,6]\n",
    "    }\n",
    "    \n",
    "    XGB_gs = GridSearchCV(XGBClassifier(), param_grid=XGB_param, n_jobs=-1, scoring='accuracy')\n",
    "    XGB_gs.fit(X_train, y_train)\n",
    "    \n",
    "    XGB_estimators = XGB_gs.best_estimator_\n",
    "    \n",
    "    return XGB_estimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 采用新的数据集：X_new_train, y_new_train\n",
    "\n",
    "# 模型交叉验证、训练，获取最优超参数\n",
    "\n",
    "LR_best_estimator = LR_gs(X_new_train, y_new_train)\n",
    "\n",
    "KNN_best_estimator = KNN_gs(X_new_train, y_new_train)\n",
    "\n",
    "SVC_best_estimator = SVC_gs(X_new_train, y_new_train)\n",
    "\n",
    "DT_best_estimator = DT_gs(X_new_train, y_new_train)\n",
    "\n",
    "RFC_best_estimator = RFC_gs(X_new_train, y_new_train)\n",
    "\n",
    "BAG_best_estimator = BAG_gs(X_new_train, y_new_train)\n",
    "\n",
    "SGD_best_estimator = SGD_gs(X_new_train, y_new_train)\n",
    "\n",
    "XGB_best_estimator = XGB_gs(X_new_train, y_new_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 预测新的数据集：X_new_test, y_new_test\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "result_df = pd.DataFrame(columns=['Accuracy', 'F1-score', 'Recall', 'Precision', 'AUC_ROC'],\n",
    "                         index=['LR','KNN','SVC','DT','RFC','Bagging','SGD','XGB'])\n",
    "\n",
    "def caculate(models, X_test, y_test):\n",
    "    # 计算各种参数的值\n",
    "    accuracy_results = []\n",
    "    F1_score_results = []\n",
    "    Recall_results = []\n",
    "    Precision_results = []\n",
    "    AUC_ROC_results = []\n",
    "    \n",
    "    for model in models:\n",
    "        y_pred = model.predict(X_test)\n",
    "        accuracy = accuracy_score(y_test, y_pred) # 计算准确度\n",
    "        precision, recall, f1_score, _ = precision_recall_fscore_support(y_test, y_pred) # 计算：精确度，召回率，f1_score\n",
    "        AUC_ROC = roc_auc_score(y_test, y_pred) # 计算AUC\n",
    "        \n",
    "        # 保存计算值\n",
    "        accuracy_results.append(accuracy)\n",
    "        F1_score_results.append(f1_score)\n",
    "        Recall_results.append(recall)\n",
    "        AUC_ROC_results.append(AUC_ROC)\n",
    "        Precision_results.append(precision)\n",
    "        \n",
    "    return accuracy_results, F1_score_results, Recall_results, AUC_ROC_results, Precision_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将所有最优超参数的模型放在一起\n",
    "best_models = [LR_best_estimator, KNN_best_estimator, SVC_best_estimator, DT_best_estimator, RFC_best_estimator,\n",
    "               BAG_best_estimator, SGD_best_estimator, XGB_best_estimator]\n",
    "\n",
    "# 调用函数计算各项指标值\n",
    "accuracy_results, F1_score_results, Recall_results, AUC_ROC_results, Precision_results = caculate(best_models, X_new_test, y_new_test)\n",
    "\n",
    "# 将各项值放入到DataFrame中\n",
    "result_df['Accuracy'] = accuracy_results\n",
    "result_df['F1-score'] = F1_score_results\n",
    "result_df['Recall'] = Recall_results\n",
    "result_df['Precision'] = Precision_results\n",
    "result_df['AUC_ROC'] = AUC_ROC_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>F1-score</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>AUC_ROC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LR</th>\n",
       "      <td>0.944162</td>\n",
       "      <td>[0.9473684210526315, 0.9405405405405406]</td>\n",
       "      <td>[0.9705882352941176, 0.9157894736842105]</td>\n",
       "      <td>[0.9252336448598131, 0.9666666666666667]</td>\n",
       "      <td>0.943189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.928934</td>\n",
       "      <td>[0.9339622641509434, 0.9230769230769231]</td>\n",
       "      <td>[0.9705882352941176, 0.8842105263157894]</td>\n",
       "      <td>[0.9, 0.9655172413793104]</td>\n",
       "      <td>0.927399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVC</th>\n",
       "      <td>0.944162</td>\n",
       "      <td>[0.9473684210526315, 0.9405405405405406]</td>\n",
       "      <td>[0.9705882352941176, 0.9157894736842105]</td>\n",
       "      <td>[0.9252336448598131, 0.9666666666666667]</td>\n",
       "      <td>0.943189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DT</th>\n",
       "      <td>0.908629</td>\n",
       "      <td>[0.9150943396226415, 0.901098901098901]</td>\n",
       "      <td>[0.9509803921568627, 0.8631578947368421]</td>\n",
       "      <td>[0.8818181818181818, 0.9425287356321839]</td>\n",
       "      <td>0.907069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RFC</th>\n",
       "      <td>0.923858</td>\n",
       "      <td>[0.9302325581395348, 0.9162011173184357]</td>\n",
       "      <td>[0.9803921568627451, 0.8631578947368421]</td>\n",
       "      <td>[0.8849557522123894, 0.9761904761904762]</td>\n",
       "      <td>0.921775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bagging</th>\n",
       "      <td>0.928934</td>\n",
       "      <td>[0.9345794392523364, 0.9222222222222223]</td>\n",
       "      <td>[0.9803921568627451, 0.8736842105263158]</td>\n",
       "      <td>[0.8928571428571429, 0.9764705882352941]</td>\n",
       "      <td>0.927038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SGD</th>\n",
       "      <td>0.923858</td>\n",
       "      <td>[0.9275362318840579, 0.9197860962566845]</td>\n",
       "      <td>[0.9411764705882353, 0.9052631578947369]</td>\n",
       "      <td>[0.9142857142857143, 0.9347826086956522]</td>\n",
       "      <td>0.923220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGB</th>\n",
       "      <td>0.939086</td>\n",
       "      <td>[0.9433962264150944, 0.9340659340659342]</td>\n",
       "      <td>[0.9803921568627451, 0.8947368421052632]</td>\n",
       "      <td>[0.9090909090909091, 0.9770114942528736]</td>\n",
       "      <td>0.937564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Accuracy                                  F1-score  \\\n",
       "LR       0.944162  [0.9473684210526315, 0.9405405405405406]   \n",
       "KNN      0.928934  [0.9339622641509434, 0.9230769230769231]   \n",
       "SVC      0.944162  [0.9473684210526315, 0.9405405405405406]   \n",
       "DT       0.908629   [0.9150943396226415, 0.901098901098901]   \n",
       "RFC      0.923858  [0.9302325581395348, 0.9162011173184357]   \n",
       "Bagging  0.928934  [0.9345794392523364, 0.9222222222222223]   \n",
       "SGD      0.923858  [0.9275362318840579, 0.9197860962566845]   \n",
       "XGB      0.939086  [0.9433962264150944, 0.9340659340659342]   \n",
       "\n",
       "                                           Recall  \\\n",
       "LR       [0.9705882352941176, 0.9157894736842105]   \n",
       "KNN      [0.9705882352941176, 0.8842105263157894]   \n",
       "SVC      [0.9705882352941176, 0.9157894736842105]   \n",
       "DT       [0.9509803921568627, 0.8631578947368421]   \n",
       "RFC      [0.9803921568627451, 0.8631578947368421]   \n",
       "Bagging  [0.9803921568627451, 0.8736842105263158]   \n",
       "SGD      [0.9411764705882353, 0.9052631578947369]   \n",
       "XGB      [0.9803921568627451, 0.8947368421052632]   \n",
       "\n",
       "                                        Precision   AUC_ROC  \n",
       "LR       [0.9252336448598131, 0.9666666666666667]  0.943189  \n",
       "KNN                     [0.9, 0.9655172413793104]  0.927399  \n",
       "SVC      [0.9252336448598131, 0.9666666666666667]  0.943189  \n",
       "DT       [0.8818181818181818, 0.9425287356321839]  0.907069  \n",
       "RFC      [0.8849557522123894, 0.9761904761904762]  0.921775  \n",
       "Bagging  [0.8928571428571429, 0.9764705882352941]  0.927038  \n",
       "SGD      [0.9142857142857143, 0.9347826086956522]  0.923220  \n",
       "XGB      [0.9090909090909091, 0.9770114942528736]  0.937564  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df # 显示计算结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化 AUC的评分\n",
    "g = sns.barplot('AUC_ROC', result_df.index, data=result_df, palette='hsv', orient='h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 集成学习\n",
    "\n",
    "# 根据以上AUC的结果,选择: LR 和 SVC 和 XGB 当做基模型\n",
    "\n",
    "LR_test = pd.Series(LR_best_estimator.predict(X_new_test), name = 'LR')\n",
    "\n",
    "SVC_test = pd.Series(SVC_best_estimator.predict(X_new_test),name = 'SVC')\n",
    "\n",
    "XGB_test = pd.Series(XGB_best_estimator.predict(X_new_test), name='XGB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把以上3个模型的预测结果集成起来\n",
    "ensemble_results = pd.concat([LR_test, SVC_test, XGB_test], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LR</th>\n",
       "      <th>SVC</th>\n",
       "      <th>XGB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>197 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     LR  SVC  XGB\n",
       "0     0    0    0\n",
       "1     1    1    1\n",
       "2     0    0    0\n",
       "3     1    1    1\n",
       "4     1    1    1\n",
       "..   ..  ...  ...\n",
       "192   1    1    1\n",
       "193   0    0    0\n",
       "194   0    0    0\n",
       "195   1    1    1\n",
       "196   1    1    1\n",
       "\n",
       "[197 rows x 3 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ensemble_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将上述3个模型集成起来，当做一个模型\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "\n",
    "voting_clf = VotingClassifier(estimators=[('LR', LR_best_estimator), ('SVC', SVC_best_estimator), \n",
    "                                          ('XGB', XGB_best_estimator)], n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('LR', LogisticRegression(C=10)),\n",
       "                             ('SVC', SVC(C=0.5, kernel='linear')),\n",
       "                             ('XGB',\n",
       "                              XGBClassifier(base_score=0.5, booster='gbtree',\n",
       "                                            colsample_bylevel=1,\n",
       "                                            colsample_bynode=1,\n",
       "                                            colsample_bytree=1, gamma=0,\n",
       "                                            gpu_id=-1, importance_type='gain',\n",
       "                                            interaction_constraints='',\n",
       "                                            learning_rate=0.300000012,\n",
       "                                            max_delta_step=0, max_depth=3,\n",
       "                                            min_child_weight=1, missing=nan,\n",
       "                                            monotone_constraints='()',\n",
       "                                            n_estimators=100, n_jobs=0,\n",
       "                                            num_parallel_tree=1, random_state=0,\n",
       "                                            reg_alpha=0, reg_lambda=1,\n",
       "                                            scale_pos_weight=1, subsample=1,\n",
       "                                            tree_method='exact',\n",
       "                                            validate_parameters=1,\n",
       "                                            verbosity=None))],\n",
       "                 n_jobs=-1)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 训练\n",
    "voting_clf.fit(X_new_train, y_new_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 预测\n",
    "y_final_pred = voting_clf.predict(X_new_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.98      0.95       102\n",
      "           1       0.98      0.92      0.95        95\n",
      "\n",
      "    accuracy                           0.95       197\n",
      "   macro avg       0.95      0.95      0.95       197\n",
      "weighted avg       0.95      0.95      0.95       197\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 评估结果 ： 最终集成学习预测的结果明显高于之前各个模型单独预测的结果\n",
    "\n",
    "print(classification_report(y_new_test, y_final_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 绘制学习曲线"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 用整个新的数据集进行交叉验证训练，并绘制学习曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.model_selection import learning_curve\n",
    "\n",
    "def plot_learning_curve(models, names, X, y, ylim=None, cv=None, n_jobs=-1):\n",
    "    f, ax = plt.subplots(8, 1, figsize=(20, 50))\n",
    "    if ylim is not None:\n",
    "        plt.ylim(*ylim)\n",
    "    for i in range(len(models)):\n",
    "        train_sizes, train_scores, test_scores = learning_curve(models[i], X, y, cv=cv, n_jobs=n_jobs)\n",
    "\n",
    "        train_scores_mean = np.mean(train_scores, axis=1)\n",
    "\n",
    "        test_scores_mean = np.mean(test_scores, axis=1)\n",
    "\n",
    "        ax[i].plot(train_sizes, train_scores_mean, 'o-', color=\"#ff9124\", label='Training score')\n",
    "        ax[i].plot(train_sizes, test_scores_mean, '+-', color='#2492ff', label='cross-validation score')\n",
    "\n",
    "        ax[i].set_title(names[i], fontsize=14)\n",
    "        ax[i].set_xlabel('Training size(m)')\n",
    "        ax[i].set_ylabel('Score')\n",
    "        ax[i].grid(True)\n",
    "        ax[i].legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = ShuffleSplit(n_splits=5, random_state=666, test_size=0.2) # 切分的比例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x3600 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制各个模型的学习曲线\n",
    "best_models = [LR_best_estimator, KNN_best_estimator, SVC_best_estimator, DT_best_estimator, RFC_best_estimator,\n",
    "               BAG_best_estimator, SGD_best_estimator, XGB_best_estimator]\n",
    "\n",
    "names = ['LR', 'KNN', 'SVC', 'DT', 'RFC', 'BAGGING', 'SGD', 'XGB']\n",
    "\n",
    "plot_learning_curve(best_models, names, X_under_sample, y_under_sample, cv=cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 绘制ROC曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "\n",
    "LR_pred = cross_val_predict(LR_best_estimator, X_under_sample, y_under_sample, cv=5)\n",
    "KNN_pred = cross_val_predict(KNN_best_estimator, X_under_sample, y_under_sample, cv=5)\n",
    "SVC_pred = cross_val_predict(SVC_best_estimator, X_under_sample, y_under_sample, cv=5)\n",
    "DT_pred = cross_val_predict(DT_best_estimator, X_under_sample, y_under_sample, cv=5)\n",
    "RFC_pred = cross_val_predict(RFC_best_estimator, X_under_sample, y_under_sample, cv=5)\n",
    "BAG_pred = cross_val_predict(BAG_best_estimator, X_under_sample, y_under_sample, cv=5)\n",
    "SGD_pred = cross_val_predict(SGD_best_estimator, X_under_sample, y_under_sample, cv=5)\n",
    "XGB_pred = cross_val_predict(XGB_best_estimator, X_under_sample, y_under_sample, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR roc_auc_score : 0.9258130081300815\n",
      "KNN roc_auc_score : 0.9247967479674797\n",
      "DT roc_auc_score : 0.9146341463414633\n",
      "SVC roc_auc_score : 0.926829268292683\n",
      "RFC_score : 0.9339430894308943\n",
      "BAG_score : 0.842479674796748\n",
      "SGD_score : 0.9166666666666667\n",
      "XGB_score : 0.9288617886178862\n"
     ]
    }
   ],
   "source": [
    "# 计算auc的评分\n",
    "print('LR roc_auc_score :', roc_auc_score(y_under_sample, LR_pred))\n",
    "print('KNN roc_auc_score :', roc_auc_score(y_under_sample, KNN_pred))\n",
    "print('DT roc_auc_score :', roc_auc_score(y_under_sample, DT_pred))\n",
    "print('SVC roc_auc_score :', roc_auc_score(y_under_sample, SVC_pred))\n",
    "print('RFC_score :', roc_auc_score(y_under_sample, RFC_pred))\n",
    "print('BAG_score :', roc_auc_score(y_under_sample, BAG_pred))\n",
    "print('SGD_score :', roc_auc_score(y_under_sample, SGD_pred))\n",
    "print('XGB_score :', roc_auc_score(y_under_sample, XGB_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算每个模型的 fpr, tpr\n",
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "LR_fpr, LR_tpr, LR_threshold = roc_curve(y_under_sample, LR_pred)\n",
    "\n",
    "KNN_fpr, KNN_tpr, KNN_threshold = roc_curve(y_under_sample, KNN_pred)\n",
    "\n",
    "DT_fpr, DT_tpr, DT_threshold = roc_curve(y_under_sample, DT_pred)\n",
    "\n",
    "SVC_fpr, SVC_tpr, SVC_threshold = roc_curve(y_under_sample, SVC_pred)\n",
    "\n",
    "RFC_fpr, RFC_tpr, RFC_threshold = roc_curve(y_under_sample, RFC_pred)\n",
    "\n",
    "BAG_fpr, BAG_tpr, BAG_threshold = roc_curve(y_under_sample, BAG_pred)\n",
    "\n",
    "SGD_fpr, SGD_tpr, SGD_threshold = roc_curve(y_under_sample, SGD_pred)\n",
    "\n",
    "XGB_fpr, XGB_tpr, XGB_threshold = roc_curve(y_under_sample, XGB_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 绘制roc曲线\n",
    "def graph_roc(fpr, tpr, name, score):\n",
    "    plt.figure(figsize=(16,8)) # 画布大小\n",
    "    plt.title(\"ROC Curve\", fontsize=14)\n",
    "    plt.plot(fpr, tpr, label=name+\":\"+ str(score))\n",
    "    plt.axis([-0.01, 1, 0, 1]) # 坐标轴\n",
    "    plt.xlabel(\"False Positive Rate (FPR)\", fontsize=14)\n",
    "    plt.ylabel(\"True Positive Rate (TPR)\", fontsize=14)\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1 LR AUC\n",
    "graph_roc(LR_fpr, LR_tpr, 'LR', roc_auc_score(y_under_sample, LR_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2 KNN AUC\n",
    "graph_roc(KNN_fpr, KNN_tpr, 'KNN', roc_auc_score(y_under_sample, KNN_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3 DT AUC\n",
    "graph_roc(DT_fpr, DT_tpr, 'DT', roc_auc_score(y_under_sample, DT_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 4 SVC AUC\n",
    "graph_roc(SVC_fpr, SVC_tpr, 'SVC', roc_auc_score(y_under_sample, SVC_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 5 RFC AUC\n",
    "graph_roc(RFC_fpr, RFC_tpr, 'RFC', roc_auc_score(y_under_sample, RFC_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 6 BAG AUC\n",
    "graph_roc(BAG_fpr,BAG_tpr, 'BAG', roc_auc_score(y_under_sample, BAG_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 7 SGD AUC\n",
    "graph_roc(SGD_fpr, SGD_tpr, 'SGD', roc_auc_score(y_under_sample, SGD_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 8 XGB AUC\n",
    "graph_roc(XGB_fpr, XGB_tpr, 'XGB', roc_auc_score(y_under_sample, XGB_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
